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revision, pending fixes

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Dobromir Popov
2025-09-26 10:49:45 +03:00
parent 2b09e7fb5a
commit 468a2c2a66
8 changed files with 61 additions and 53 deletions
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23
core/orchestrator.py
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@@ -1843,7 +1843,7 @@ class TradingOrchestrator:
dashboard=None
)
logger.info("✅ Enhanced training system initialized successfully")
logger.info("Enhanced training system initialized successfully")
# Auto-start training by default
logger.info("🚀 Auto-starting enhanced real-time training...")
@@ -2204,21 +2204,12 @@ class TradingOrchestrator:
return float(data_stream.current_price)
except Exception as e:
logger.debug(f"Could not get price from universal adapter: {e}")
# Fallback to default prices
default_prices = {
'ETH/USDT': 2500.0,
'BTC/USDT': 108000.0
}
return default_prices.get(symbol, 1000.0)
# TODO(Guideline: no synthetic fallback) Provide a real-time or cached market price here instead of hardcoding.
raise RuntimeError("Current price unavailable; per guidelines do not substitute synthetic values.")
except Exception as e:
logger.error(f"Error getting current price for {symbol}: {e}")
# Return default price based on symbol
if 'ETH' in symbol:
return 2500.0
elif 'BTC' in symbol:
return 108000.0
else:
return 1000.0
raise RuntimeError("Current price unavailable; per guidelines do not substitute synthetic values.")
# SINGLE-USE FUNCTION - Called only once in codebase
def _generate_fallback_prediction(self, symbol: str) -> Dict[str, Any]:
@@ -2443,7 +2434,7 @@ class TradingOrchestrator:
if df is not None and not df.empty:
loaded_data[f"{symbol}_{timeframe}"] = df
total_candles += len(df)
logger.info(f"✅ Loaded {len(df)} {timeframe} candles for {symbol}")
logger.info(f"Loaded {len(df)} {timeframe} candles for {symbol}")
# Store in data provider's historical cache for quick access
cache_key = f"{symbol}_{timeframe}_300"
@@ -2500,7 +2491,7 @@ class TradingOrchestrator:
logger.info("Initializing Decision Fusion with multi-symbol features...")
self._initialize_decision_with_provider_data(symbol_features)
logger.info("✅ All models initialized with data provider's normalized historical data")
logger.info("All models initialized with data provider's normalized historical data")
except Exception as e:
logger.error(f"Error initializing models with historical data: {e}")
@@ -2720,7 +2711,7 @@ class TradingOrchestrator:
logger.error(f"Error in chained inference step {step}: {e}")
break
logger.info(f"✅ Chained inference completed: {len(predictions)} predictions generated")
logger.info(f"Chained inference completed: {len(predictions)} predictions generated")
return predictions
except Exception as e: